Analytics Tooling

Enterprises are increasingly relying on dbt (Data Build Tool) for their data analytics; however, dbt wasn’t designed to be an enterprise-ready platform on its own. This leads to struggles with scalability, orchestration, governance, and operational efficiency when implementing dbt at scale. But if dbt is so amazing why is this the case? Like our title suggests, you need more than just dbt to have a successful dbt analytics implementation. Keep on reading to learn exactly what you need to super charge your data analytics with dbt successfully.  

Why Enterprises Adopt dbt for Data Transformation

dbt is popular because it solves problems facing the data analytics world. Enterprises today are dealing with growing volumes of data, making efficient data transformation a critical part of their analytics strategy. Traditionally, data transformation was handled using complex ETL (Extract, Transform, Load) processes, where data engineers wrote custom scripts to clean, structure, and prepare data before loading it into a warehouse. However, this approach has several challenges:

• Slow Development Cycles – ETL processes often required significant engineering effort, creating bottlenecks and slowing down analytics workflows.

• High Dependency on Engineers – Analysts and business users had to rely on data engineers to implement transformations, limiting agility.

• Difficult Collaboration & Maintenance – Custom scripts and siloed processes made it hard to track changes, ensure consistency, and maintain documentation.

dbt (Data Build Tool) transforms this paradigm by enabling SQL-based, modular, and version-controlled transformations directly inside the data warehouse. By following the ELT (Extract, Load, Transform) approach, dbt allows raw data to be loaded into the warehouse first, then transformed within the warehouse itself—leveraging the scalability and processing power of modern cloud data platforms.

Unlike traditional ETL tools, dbt applies software engineering best practices to SQL-based transformations, making it easier to develop, test, document, and scale data pipelines. This shift has made dbt a preferred solution for enterprises looking to empower analysts, improve collaboration, and create maintainable data workflows.

Key Benefits of dbt

• SQL-Based Transformations – dbt enables data teams to perform transformations within the data warehouse using standard SQL. By managing the Data Manipulation Language (DML) statements, dbt allows anyone with SQL skills to contribute to data modeling, making it more accessible to analysts and reducing reliance on specialized engineering resources.

• Automated Testing & Documentation – With more people contributing to data modeling things can become a mess but dbt shines by incorporating automated testing and documentation to ensure data reliability. With dbt  teams can have a decentralized development pattern but maintain centralized governance.  

• Version Control & Collaboration – Borrowing from software engineering best practices dbt enables teams to track changes using Git. Any changes made to data models can be clearly tracked and reverted, simplifying collaboration.  

• Modular and Reusable Code – dbt's powerful combination of SQL and Jinja enables the creation of modular and reusable code, significantly enhancing maintainability. Using Jinja, dbt allows users to define macros—reusable code snippets that encapsulate complex logic. This means less redundancies and consistent application of business rules across models.  

• Scalability & Performance Optimization – dbt leverages the data warehouse’s native processing power, enabling incremental models that minimize recomputation and improve efficiency.

• Extensibility & Ecosystem – dbt integrates with orchestration tools (e.g., Airflow) and metadata platforms (e.g., DataHub), supporting a growing ecosystem of plugins and APIs.

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With these benefits it is clear why over 40,000 companies are leveraging dbt today!

The Challenges of Scaling dbt in the Enterprise

Despite dbt’s strengths, enterprises face several challenges when implementing it at scale for a variety of reasons:

Complexity of Scaling dbt

Running dbt in production requires robust orchestration beyond simple scheduled jobs. dbt only manages transformations, but a complete end-to-end pipeline includes Extracting, Loading and Visualizing of data. To manage the full end-to-end data pipeline (ELT + Viz) organizations will need a full-fledged orchestrator like Airflow. While there are other orchestration options on the market,  Airflow and dbt are a common pattern.  

Lack of Integrated CI/CD & Development Controls

CI/CD pipelines are essential for dbt at the enterprise level, yet one of dbt Core’s major limitations is the lack of a built-in CI/CD pipeline for managing deployments. This makes workflows more complex and increases the likelihood of errors reaching production. To address this, teams can implement external tools like Jenkins, GitHub Actions, or GitLab Workflows that provide a flexible and customizable CI/CD process to automate deployments and enforce best practices.

While dbt Cloud does offer an out-of-the-box CI/CD solution, it lacks customization options. Some organizations find that their use cases demand greater flexibility, requiring them to build their own CI/CD processes instead.

Infrastructure & Deployment Constraints

Enterprises seek alternative solutions that provide greater control, scalability, and security over their data platform. However, this comes with the responsibility of managing their own infrastructure, which introduces significant operational overhead ($$$). Solutions like dbt Cloud do not offer Virtual Private Cloud (VPC) deployment, full CI/CD flexibility, and a fully-fledged orchestrator leaving organizations to handle additional platform components.

We saw a need for a middle ground that combined the best of both worlds; something as flexible as dbt Core and Airflow, but fully managed like dbt Cloud. This led to Datacoves which provides a seamless experience with no platform maintenance overhead or  onboarding hassles. Teams can focus on generating insights from data and not worry about the platform.

Avoiding Vendor Lock-In

Vendor lock-in is a major concern for organizations that want to maintain flexibility and avoid being tied to a single provider. The ability to switch out tools easily without excessive cost or effort is a key advantage of the modern data stack. Enterprises benefit from mixing and matching best-in-class solutions that meet their specific needs.

How Datacoves Solves Enterprise dbt Challenges

Datacoves is a fully managed enterprise platform for dbt, solving the challenges outlined above. Below is how Datacoves' features align with enterprise needs:

Platform Capabilities

• Integrated Development Environment (IDE): With in-browser VS Code, users can develop SQL and Python seamlessly within a browser-based VS Code environment. This includes full access to the terminal, python libraries, and VS Code extensions for the most customizable development experience.  

• Managed Development Environment: Pre-configured VS Code, dbt, and Airflow setup for enterprise teams. Everything is managed so project leads dont have to worry about dependencies, docker images, upgrades or onboarding. Datacoves users can be onboarded to a new project it minutes not days.  

• Scalability & Flexibility: Kubernetes-powered infrastructure for elastic scaling. Users don’t have the operational overhead of managing their dbt and Airflow environments, they simply login and everything just works.  

• Version Control & Collaboration: Datacoves integrates seamlessly with Git services like Github, Gitlab, Bitbucket, and Azure DevOps. When deployed in the customer’s VPC, Datacoves can even access private Git servers and Docker registries.

• Security & User Management: Datacoves can integrate Single Sign-On (SSO) for authentication., and AD groups for role management.

• Use of Open-Source Tools: Built on standard dbt Core, Airflow, and VS Code to ensure maximum flexibility. At the end of the day it is your code and you can take it with you.  

Data Extraction and Loading

• With Datacoves, companies can leverage a managed Airbyte instance out of the box. However, if users are not using Airbyte or need addition EL tools Datacoves seamlessly integrates with enterprise EL solutions such as Amazon Glue, Azure Data Factory, Databricks, Streamsets, etc. Additionally, since Datacoves supports Python development organizations can leverage their custom Python frameworks or develop using tools like dlt (data load tool) with ease.

Data Transformation

• Support for SQL & Python: In addition to SQL or Python modeling via dbt, users can develop non-dbt Python scripts right within VS Code.

• Data Warehouse & Data Lake Support: As a platform, Datacoves is warehouse agnostic. It works with Snowflake, BigQuery, Redshift, Databricks, MS Fabric, and any other dbt-compatible warehouse.  

Pipeline Orchestration

• Enterprise-Grade Managed Apache Airflow: By adopting a full fledged orchestrator, developers can orchestrate the full ELT + Viz pipeline minimizing cost and pipeline failures. One of the biggest benefits of Datacoves is its fully managed Airflow scheduler for data pipeline orchestration. Developers don’t have to worry about the infrastructure overhead or scaling headaches of managing their own Airflow.

• Developer Instance of Airflow ("My Airflow"): With a few clicks easily stand-up a solo Sandbox Airflow instance for testing DAGs before deployment. My Airflow can speed up DAG development by 20%+!

• Orchestrator Flexibility & Extensibility: Datacoves provides templated accelerators for creating Airflow DAGs and managing dbt runs. These best practices can be invaluable to an organization getting started or looking to optimize.

• Alerting & Monitoring: Out of the box SMTP integration as well as support for custom SMTP, Slack, and Microsoft Teams notifications for proactive monitoring.  

Data Quality and Governance

• Cross-project lineage via Datacoves Mesh (aka dbt Mesh): Have a large dbt project that would benefit by being split into multiple projects? Datacoves enables large-scale cross-team collaboration with cross dbt project support.

• Enterprise-Grade Data Catalog (Datahub): Datacoves provides an optionally hosted Datahub instance which comes with Column-level lineage for tracking data transformations and includes cross project column-level lineage support.

• CI/CD Accelerators: Need a robust CI/CD pipeline? Datacoves provides accelerator scripts for Jenkins, Github Actions, and Gitlab workflows so teams dont start at square one. These scripts are fully customizable to meet any team’s needs.

• Enterprise Ready RBAC: Datacoves provides tools and processes that simplify Snowflake permissions while maintainig the controls necessary for securing PII data and complying with GDPR and CCPA regulations.

Licensing and Pricing Plans

Datacoves offers flexible deployment and pricing options to accommodate various enterprise needs:

• Deployment Options: Choose between Datacoves' multi-tenant SaaS platform or a customer-hosted Virtual Private Cloud (VPC) deployment, ensuring compliance with security and regulatory requirements.  

• Scalable Pricing: Pricing structures are designed to scale to enterprise levels, optimizing costs as your data operations grow.

• Total Cost of Ownership (TCO): By providing a fully managed environment for dbt and Airflow, Datacoves reduces the need for in-house infrastructure management, lowering TCO by up to 50%.  

Vendor Information and Support

Datacoves is committed to delivering enterprise-grade support and resources through our white-glove service:

• Dedicated Support: Comprehensive support packages, providing direct access to Datacoves' development team for timely assistance in Teams, Slack, and or email.  

• Documentation and Training: Extensive documentation and optional training packages to help teams effectively utilize the platform.  

• Change Management Expertise: We know that true adoption does not lie with the tools but rather change management. As a thought leader on the subject, Datacoves has guided many organizations through the implementation and scaling of dbt, ensuring a smooth transition and adoption of best practices.  

Conclusion

Enterprises need more than just dbt to achieve scalable and efficient analytics. While dbt is a powerful tool for data transformation, it lacks the necessary infrastructure, governance, and orchestration capabilities required for enterprise-level deployments. Datacoves fills these gaps by providing a fully managed environment that integrates dbt-Core, VS Code, Airflow, and Kubernetes-based deployments, Datacoves is the ultimate solution for organizations looking to scale dbt successfully.  

The latest release of dbt 1.9, introduces some exciting features and updates meant to enhance functionality and tackle some pain points of dbt. With improvements like microbatch incremental strategy, snapshot enhancements, Iceberg table format support, and streamlined CI workflows, dbt 1.9 continues to help data teams work smarter, faster, and with greater precision. All the more reason to start using dbt today!  

We looked through the release notes, so you don’t have to. This article highlights the key updates in dbt 1.9, giving you the insights needed to upgrade confidently and unlock new possibilities for your data workflows. If you need a flexible dbt and Airflow experience, Datacoves might be right for your organization. Lower total cost of ownership by 50% and shortened your time to market today!

Compatibility Note: Upgrading from Older Versions

If you are upgrading from dbt 1.7 or earlier, you will need to install both dbt-core and the appropriate adapter. This requirement stems from the decoupling introduced in dbt 1.8, a change that enhances modularity and flexibility in dbt’s architecture. These updates demonstrate dbt’s commitment to providing a streamlined and adaptable experience for its users while ensuring compatibility with modern tools and workflows.

pip install dbt-core dbt-snowflake

Microbatch Incremental Strategy: A Better Way to Handle Large Data

In dbt 1.9, the microbatch incremental strategy is a new way to process massive datasets. In earlier versions of dbt, incremental materialization was available to process datasets which were too large to drop and recreate at every build. However, it struggled to efficiently manage very large datasets that are too large to fit into one query. This limitation led to timeouts and complex query management.

The microbatch incremental strategy comes to the rescue by breaking large datasets into smaller chunks for processing using the batch_size, event_time, and lookback configurations to automatically generate the necessary filters for you. However, at the time of this publication this feature is only available on the following adapters: Postgres, Redshift, Snowflake, BigQuery, Spark, and Databricks, with more on the way.  

Key Benefits of Microbatching

• Simplified Query Design: As mentioned earlier, dbt will handle the logic for your batch data using simple, yet powerful configurations. By setting the event_time, lookback, and batch_size configurations dbt will generate the necessary filters for each batch. One less thing to worry about!  

• Independent Batch Processing: dbt automatically splits your data into smaller chunks based on the batch_size you set. Each batch is processed separately and in parallel, unless you disable this feature using the +concurrent_batches config. This independence in batch processing improves performance, minimizes the risk of query failures, allows you to retry failed batches using the dbt retry command, and provides the granularity to load specific batches. Gotta love the control without the extra leg work!

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Compatibility Note:  Custom microbatch macros

To take advantage of the microbatch incremental strategy, first upgrade to dbt 1.9 and ensure your project is configured correctly. By default, dbt will handle the microbatch logic for you, as explained above. However, if you’re using custom logic, such as a custom microbatch macro, don’t forget to  set the require_batched_execution_for_custom_microbatch_strategy behavior flag to True in your dbt_project.yml file. This prevents deprecation warnings and ensures dbt knows how to handle your custom configuration.

If you have custom microbatch but wish to migrate, its important to note that earlier versions required setting the environment variable DBT_EXPERIMENTAL_MICROBATCH to enable microbatching, but this is no longer needed. Starting with Core 1.9, the microbatch strategy works seamlessly out of the box, so you can remove it.

Enhanced Snapshots: Smarter and More Flexible Data Tracking

With dbt 1.9, snapshots have become easier to use than ever! This is great news for dbt users since snapshots in dbt allow you to capture the state of your data at specific points in time, helping you track historical changes and maintain a clear picture of how your data evolves.  Below are a couple of improvements to implement or be aware of.

Key Improvements in Snapshots

• YAML Configurations: Snapshots can now be defined directly in YAML files. This makes them easier to manage, read, and update, allowing for a more streamlined configuration process that aligns with other dbt project components. Lots of things are easier in YAML. 😉

• Customizable Metadata Fields: With the snapshot_meta_column_names config you now have the option to rename metadata fields to match your project's naming conventions. This added flexibility helps ensure consistency across your data models and simplifies collaboration within teams.  

• Default target_schema: If you do not specify a schema for your snapshots, dbt will use the schema defined for the current environment. This means that snapshots will be created in the default schema associated with your dbt environment settings.

• Standardization of resource type: Snapshots now support the standard schema and database configurations, similar to models and seeds. This standardization allows you to define where your snapshots are stored using familiar configuration patterns.

• New Warnings: You will now get a warning if you set an incorrect updated_at data type. This ensures it is an accepted data type or timestamp. No more silent error.  

• Set an expiration date: Before dbt 1.9 the dbt_valid_to variable is set to NULL but you can now you can configure it to a data with the dbt_valid_to_current config. It is important to note that dbt will not automatically adjust the current value in the existing dbt_valid_to column. Meaning, any existing current records will still have dbt_valid_to set to NULL and new records will have this value set to your configured date.  You will have to manually update existing data to match. Less NULL values to handle downstream!  

• dbt snapshot–empty: In dbt 1.9, the --empty flag is now supported for the dbt snapshot command, allowing you to execute snapshot operations without processing data. This enhancement is particularly useful in Continuous Integration (CI) environments, enabling the execution of unit tests for models downstream of snapshots without requiring actual data processing, streamlining the testing process. The empty flag, introduced in dbt 1.8, also has some powerful applications in Slim CI to optimize your CI/CD worth checking out.

• Improved Handling of Deleted Records: In dbt version 1.9, the hard_deletes configuration enhances the management of deleted records in snapshots. This feature offers three methods: the default ignore, which takes no action on deleted records; invalidate, replacing the invalidate_hard_deletes=trueconfig, which marks deleted records as invalid by setting their dbt_valid_to timestamp to the current time; and lastly new_record, which tracks deletions by inserting a new record with a dbt_is_deleted config set to True.  

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Compatibility Note:  hard_deletes

It's important to note some migration efforts will be required for this. While the invalidate_hard_deletes configuration is still supported for existing snapshots, it cannot be used alongside hard_deletes. For new snapshots, it's recommended to use hard_deletes instead of the legacy invalidate_hard_deletes. If you switch an existing snapshot to use hard_deletes without migrating your data, you may encounter inconsistent or incorrect results, such as a mix of old and new data formats. Keep this in mind when implementing these new configs.

Unit Testing Enhancements: Streamlined Testing for Better Data Quality

Testing is a vital part of maintaining high data quality and ensuring your data models work as intended. Unit testing was introduced in dbt 1.8 and has seen continued improvement in dbt 1.9.  

Key Enhancements in Unit Testing:

• Selective Testing with Unit Test Selectors: dbt 1.9 introduces a new selection method for unit tests, allowing users to target specific unit tests directly using the unit_test: selector. This feature enables more granular control over test execution, allowing you to focus on particular tests without running the entire suite, thereby saving time and resources.

dbt test --select unit_test:my_project.my_unit_test 

dbt build --select unit_test:my_project.my_unit_test 

• Improved Resource Type Handling: The update ensures that commands like dbt list --resource-type test now correctly include only data tests, excluding unit tests. This distinction enhances clarity and precision when managing different test types within your project.  

dbt ls --select unit_test:my_project.my_unit_test 

Slim CI State Modifications: Smarter and More Accurate Workflows

In dbt version 1.9, the state:modified selector has been enhanced to improve the accuracy of Slim CI workflows. Previously, dynamic configurations—such as setting the database based on the environment—could lead to dbt perceiving changes in models, even when the actual model remained unchanged. This misinterpretation caused Slim CI to rebuild all models unnecessarily, resulting in false positives.

By comparing unrendered configuration values, dbt now accurately detects genuine modifications, eliminating false positives during state comparisons. This improvement ensures that only truly modified models are selected for rebuilding, streamlining your CI processes.

Key Benefits:

• Improved Accuracy: Focusing on unrendered configurations reduces false positives during state comparisons.

• Streamlined CI Processes: Enhanced change detection allows CI workflows to concentrate solely on resources that require updates or testing.

• Time and Resource Efficiency: Minimizing unnecessary computations conserves both time and computational resources.

To enable this feature, set the state_modified_compare_more_unrendered_values flag to True in your dbt_project.yml file:

flags: 
	state_modified_compare_more_unrendered_values: True 

Enhanced Documentation Hosting with --host Flag in dbt 1.9

In dbt 1.9, the dbt docs serve command now has more customization abilities with a new --host flag. This flag allows users to specify the host address for serving documentation. Previously, dbt docs serve defaulted to binding the server to 127.0.0.1 (localhost) without an option to override this setting.  

Users can now specify a custom host address using the --host flag when running dbt docs serve. This enhancement provides the flexibility to bind the documentation server to any desired address, accommodating various deployment needs. The default of the --host flag will continue to bind to 127.0.0.1 by default, ensuring backward compatibility and secure defaults.

Key Benefits:

• Deployment Flexibility: Users can bind the documentation server to different host addresses as required by their deployment environment.

• Improved Accessibility: Facilitates access to dbt documentation across various network configurations by enabling custom host bindings.

• Enhanced Compatibility: Addresses previous limitations and resolves issues encountered in deployments that require non-default host bindings.

Other Notable Improvements in dbt 1.9

dbt 1.9 includes several updates aimed at improving performance, usability, and compatibility across projects. These changes ensure a smoother experience for users while keeping dbt aligned with modern standards.

• Iceburg table  support: With dbt 1.9, you can now add Iceberg table support to table, incremental, dynamic table materializations.

• Optimized dbt clone Performance: The dbt clone command now executes clone operations concurrently, enhancing efficiency and reducing execution time.

• Parseable JSON and Text Output in Quiet Mode: The dbt show and dbt compile commands now support parseable JSON and text outputs when run in quiet mode, facilitating easier integration with other tools and scripts by providing machine-readable outputs.

• skip_nodes_if_on_run_start_fails Behavior Change Flag: A new behavior change flag, skip_nodes_if_on_run_start_fails, has been introduced to gracefully handle failures in on-run-start hooks. When enabled, if an on-run-start hook fails, subsequent hooks and nodes are skipped, preventing partial or inconsistent runs.

Compatibility Note:  Sans Python 3.8

• Python 3.8 Support Removed: dbt 1.9 no longer supports Python 3.8, encouraging users to upgrade to newer Python versions. This ensures compatibility with the latest features and enhances overall performance.  

Conclusion

dbt 1.9 introduces a range of powerful features and enhancements, reaffirming its role as a cornerstone tool for modern data transformations.  The enhancements in this release reflect the community's commitment to innovation and excellence as well as its strength and vitality. There's no better time to join this dynamic ecosystem and elevate your data workflows!

If you're looking to implement dbt efficiently, consider partnering with Datacoves. We can help you reduce your total cost of ownership by 50% and accelerate your time to market. Book a call with us today to discover how we can help your organization in building a modern data stack with minimal technical debt.

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Checkout the full release notes.

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dbt and Airflow are cornerstone tools in the modern data stack, each excelling in different areas of data workflows. Together, dbt and Airflow provide the flexibility and scalability needed to handle complex, end-to-end workflows.

This article delves into what dbt and Airflow are, why they work so well together, and the challenges teams face when managing them independently. It also explores how Datacoves offers a fully managed solution that simplifies operations, allowing organizations to focus on delivering actionable insights rather than managing infrastructure.

What is dbt?

dbt (Data Build Tool) is an open-source analytics engineering framework that transforms raw data into analysis-ready datasets using SQL. It enables teams to write modular, version-controlled workflows that are easy to test and document, bridging the gap between analysts and engineers.

• Adoption: With over 40,000 companies using dbt, the majority rely on open-source dbt Core available to anyone.

• Key Strength: dbt empowers anyone with SQL knowledge to own the logic behind data transformations, giving them control over cleansing data and delivering actionable insights.

• Key Weakness: Teams using open-source dbt on their own must manage infrastructure, developer environments, job scheduling, documentation hosting, and the integration of tools for loading data into their data warehouse.  

What is Airflow?

Apache Airflow is an open-source platform designed to orchestrate workflows and automate tasks. Initially created for ETL processes, it has evolved into a versatile solution for managing any sequence of tasks in data engineering, machine learning, or beyond.

• Adoption: With over 37,000 stars on GitHub, Airflow is one of the most popular orchestration tools, seeing thousands of downloads every month.

• Key strength: Airflow excels at handling diverse workflows. Organizations use it to orchestrate tools like Azure Data Factory, Amazon Glue, and open-source options like dlt (data load tool). Airflow can trigger dbt transformations, post-transformation processes like refreshing dashboards, or even marketing automation tasks. Its versatility extends to orchestrating AI and ML pipelines, making it a go-to solution for modern data stacks.

• Key weakness: Scaling Airflow often requires running it on Kubernetes for its scalable nature. However, this introduces significant operational overhead and a steep learning curve to configure and maintain the Kubernetes cluster.

Why dbt and Airflow are a natural pair

Stitch together disjointed schedules

While dbt excels at SQL-based data transformations, it has no built-in scheduler, and solutions like dbt Cloud’s scheduling capabilities are limited to triggering jobs in isolation or getting a trigger from an external source. This approach risks running transformations on stale or incomplete data if upstream processes fail. Airflow eliminates this risk by orchestrating tasks across the entire pipeline, ensuring transformations occur at the right time as part of a cohesive, integrated workflow.

Tools like Airbyte and Fivetran also provide built-in schedulers, but these are designed for loading data at a given time and optionally trigger a dbt pipeline. As complexity grows and organizations need to trigger dbt pipelines after data loads via different means such as dlt and Fivetran, then this simple approach does not scale. It is also common to trigger operations after a dbt pipeline and scheduling using the data loading tool will not handle that complexity. With dbt and Airflow, a team can connect the entire process and assure that processes don’t run if upstream tasks fail or are delayed.

Airflow centralizes orchestration, automating the timing and dependencies of tasks—extracting and loading data, running dbt transformations, and delivering outputs. This connected approach reduces inefficiencies and ensures workflows run smoothly with minimal manual intervention.

Handle complexity with ease

Modern data workflows extend beyond SQL transformations. Airflow complements dbt by supporting complex, multi-stage processes such as integrating APIs, executing Python scripts, and training machine learning models. This flexibility allows pipelines to adapt as organizational needs evolve.

Airflow also provides a centralized view of pipeline health, offering data teams complete visibility. With its ability to trace issues and manage dependencies, Airflow helps prevent cascading failures and keeps workflows reliable.

By combining dbt’s transformation strengths with Airflow’s orchestration capabilities, teams can move past fragmented processes. Together, these tools enable scalable, efficient analytics workflows, helping organizations focus on delivering actionable insights without being bogged down by operational hurdles.

Managed Airflow and managed dbt in Datacoves

In our previous article, we discussed building vs buying your Airflow and dbt infrastructure. There are many cons associated with self-hosting these two tools, but Datacoves takes the complexity out of managing dbt and Airflow by offering a fully integrated, managed solution. Datacoves has given many organizations the flexibility of open-source tools with the freedom of managed tools. See how we helped Johnson and Johnson MedTech migrate to our managed dbt and airflow platform.

Managed dbt

Datacoves offers the most flexible and robust managed dbt Core environment on the market, enabling teams to fully harness the power of dbt without the complexities of infrastructure management, environment setup, or upgrades. Here’s why our customers choose Datacoves to implement dbt:

• Seamless VS Code environment: Users can log in to a secure, browser-based VS Code development environment and start working immediately. With access to the terminal, VS Code extensions, packages, and libraries, developers have the full power of the tools they already know and love—without the hassle of managing local setups. Unlike inflexible custom IDEs, the familiar and flexible VS Code environment empowers developers to work efficiently. Scaling and onboarding new analytics engineers is streamlined so they can be productive in minutes.

• Optimized for dbt development: Datacoves is designed to enhance the dbt development experience with features like SQL formatting, autocomplete, linting, compiled dbt preview, curated extensions, and python libraries. It ensures teams can develop efficiently and maintain high standards for their project.

• Effortless upgrade management: Datacoves manages both platform and version upgrades. Upgrades require minimal work from the data teams and is usually as simple as “change this line in this file.”  

• CI/CD accelerators: Many teams turn to Datacoves after outgrowing the basic capabilities of dbt Cloud CI. Datacoves integrates seamlessly with leading CI/CD tools like GitHub Actions, GitLab Workflows, and Jenkins. But we don’t stop at providing the tools—we understand that setting up and optimizing these pipelines requires expertise. That’s why we work closely with our customers to implement robust CI/CD pipelines, saving them valuable time and reducing costs.

• dbt best practices and guidance: Datacoves provides accelerators and starting points for dbt projects, offering teams a strong foundation to begin their work or improve their project following best practices. This approach has helped teams minimize technical debt and ensure long-term project success. As an active and engaged member of the dbt community, Datacoves stays up to date on new improvements and changes. Supporting customers by providing expert guidance on required updates and optimizations.

Managed Airflow

Datacoves offers a fully managed Airflow environment, designed for scalability, reliability, and simplicity. Whether you're orchestrating complex ETL workflows, triggering dbt transformations, or integrating with third-party APIs, Datacoves takes care of the heavy lifting by managing the Kubernetes infrastructure, monitoring, and scaling. Here’s what sets Datacoves apart as a managed Airflow solution:

• Multiple Airflow environments: Teams can seamlessly access their hosted Airflow UI and easily set up dedicated development and production instances. Managing secrets is simplified with secure options like Datacoves Secrets Manager or AWS Secrets Manager, enabling a streamlined and secure workflow without the logistical headaches.

• Observability: With built-in tools like Grafana, teams gain comprehensive visibility into their Airflow jobs and workflows. Monitor performance, identify bottlenecks, and troubleshoot issues with ease—all without the operational overhead of managing Kubernetes clusters or runtime errors.

• Upgrade management: Upgrading Airflow is simple and seamless. Teams can transition to newer Airflow versions without downtime or complexity.

• Git sync/S3 sync: Users can effortlessly sync their Airflow DAGs using two popular methods—Git Sync or S3 Sync—without needing to worry about the complexities of setup or configuration.

• My Airflow: Datacoves offers My Airflow, a standalone Airflow instance that lets users instantly test and develop DAGs at the push of a button. This feature provides developers with the freedom to experiment safely without affecting development or production Airflow instances.

• Airflow best practices and guidance: Datacoves provides expert guidance on DAG optimization and Airflow best practices, ensuring your organization avoids costly technical debt and gets it right from the start.

Conclusion

dbt and Airflow are a natural pair in the Modern Data Stack. dbt’s powerful SQL-based transformations enable teams to build clean, reliable datasets, while Airflow orchestrates these transformations within a larger, cohesive pipeline. Their combination allows teams to focus on delivering actionable insights rather than managing disjointed processes or stale data.

However, managing these tools independently can introduce challenges, from infrastructure setup to scaling and ongoing maintenance. That’s where platforms like Datacoves make a difference. For organizations seeking to unlock the full potential of dbt and Airflow without the operational overhead, solutions like Datacoves provide the scalability and efficiency needed to modernize data workflows and accelerate insights.

‍Book a call today to see how Datacoves can help your organization realize the power of Airflow and dbt.

Not long ago, the data analytics world relied on monolithic infrastructures—tightly coupled systems that were difficult to scale, maintain, and adapt to changing needs. These legacy setups often resulted in operational bottlenecks, delayed insights, and high maintenance costs. To overcome these challenges, the industry shifted toward what was deemed the Modern Data Stack (MDS)—a suite of focused tools optimized for specific stages of the data engineering lifecycle.

This modular approach was revolutionary, allowing organizations to select best-in-class tools like Airflow for Orchestration or a managed version of Airflow from Astronomer or Amazon without the need to build custom solutions. While the MDS improved scalability, reduced complexity, and enhanced flexibility, it also reshaped the build vs. buy decision for analytics platforms. Today, instead of deciding whether to create a component from scratch, data teams face a new question: Should they build the infrastructure to host open-source tools like Apache Airflow and dbt Core, or purchase their managed counterparts? This article focuses on these two components because pipeline orchestration and data transformation lie at the heart of any organization’s data platform.

What does it mean to build vs buy?

Build

When we say build in terms of open-source solutions, we mean building infrastructure to self-host and manage mature open-source tools like Airflow and dbt. These two tools are popular because they have been vetted by thousands of companies! In addition to hosting and managing, engineers must also ensure interoperability of these tools within their stack, handle security, scalability, and reliability. Needless to say, building is a huge undertaking that should not be taken lightly.

Buy

dbt and Airflow both started out as open-source tools, which were freely available to use due to their permissive licensing terms. Over time, cloud-based managed offerings of these tools were launched to simplify the setup and development process. These managed solutions build upon the open-source foundation, incorporating proprietary features like enhanced user interfaces, automation, security integration, and scalability. The goal is to make the tools more convenient and reduce the burden of maintaining infrastructure while lowering overall development costs. In other words, paid versions arose out of the pain points of self-managing the open-source tools.

This begs the important question: Should you self-manage or pay for your open-source analytics tools?

Comparing build vs. buy: Key tradeoffs  

As with most things, both options come with trade-offs, and the “right” decision depends on your organization’s needs, resources, and priorities. By understanding the pros and cons of each approach, you can choose the option that aligns with your goals, budget, and long-term vision.

Building In-House  

Pros:  

• Customization: The biggest advantage of building in-house is the flexibility to customize the tool to fit your exact use case. You maintain full control, allowing you to align configurations with your organization’s unique needs. However, with great power comes great responsibility—your team must have a deep understanding of the tools, their options, and best practices.  

• Control: Owning the entire stack gives your team the ability to integrate deeply with existing systems and workflows, ensuring seamless operation within your ecosystem.  

• Cost Perception: Without licensing fees, building in-house may initially appear more cost-effective, particularly for smaller-scale deployments.  

Cons:  

• High Upfront Investment: Setting up infrastructure requires a considerable time commitment from developers. Tasks like configuring environments, integrating tools like Git or S3 for Airflow DAG syncing, and debugging can consume weeks of developer hours.  

• Operational Complexity: Ongoing maintenance—such as managing dependencies, handling upgrades, and ensuring reliability—can be overwhelming, especially as the system grows in complexity.  

• Skill Gaps: Many teams underestimate the level of expertise needed to manage Kubernetes clusters, Python virtual environments, and secure credential storage systems like AWS Secrets Manager.  

• Experimentation: Your organization is using the first iteration the team is producing which can lead to unintended consequences, edge cases, and security issues.  

Example:

A team building Airflow in-house may spend weeks configuring a Kubernetes-backed deployment, managing Python dependencies, and setting up DAG synchronizing files via S3 or Git. While the outcome can be tailored to their needs, the time and expertise required represent a significant investment.  

Building with open-source is not free. Cons Continued

Before moving on to the buy tradeoffs, it is important to set the record straight. You may have noticed that we did not include “the tool is free to use” as one of our pros for building with open-source. You might have guessed by reading the title of this section, but many people incorrectly believe that building their MDS using open-source tools like dbt is free. When in reality there are many factors that contribute to the true dbt pricing and the same is true for Airflow.

How can that be?  Well, setting up everything you need and managing infrastructure for Airflow and dbt isn’t necessarily plug and play.  There is day-to-day work from managing Python virtual environments, keeping dependencies in check, and tackling scaling challenges which require ongoing expertise and attention. Hiring a team to handle this will be critical particularly as you scale. High salaries and benefits are needed to avoid costly mistakes; this can easily cost anywhere from $5,000 to $26,000+/month depending on the size of your team.

In addition to the cost of salaries, let’s look at other possible hidden costs that come with using open-source tools.  

Time and expertise  

The time it takes to configure, customize, and maintain a complex open-source solution is often underestimated. It’s not until your team is deep in the weeds—resolving issues, figuring out integrations, and troubleshooting configurations—that the actual costs start to surface. With each passing day your ROI is threatened. You want to start gathering insights from your data as soon as possible. Datacoves helped Johnson and Johnson set up their data stack in weeks and when issues arise, a you will need expertise to accelerate the time to resolution.

And then there’s the learning curve. Not all engineers on your team will be seniors, and turnover is inevitable. New hires will need time to get up to speed before they can contribute effectively. This is the human side of technology: while the tools themselves might move fast, people don’t. That ramp-up period, filled with training and trial-and-error, represents a hidden cost.  

Security and compliance  

Security and compliance add another layer of complexity. With open-source tools, your team is responsible for implementing best practices—like securely managing sensitive credentials with a solution like AWS Secrets Manager. Unlike managed solutions, these features don’t come prepackaged and need to be integrated with the system.  

Compliance is no different. Ensuring your solution meets enterprise governance requirements takes time, research, and careful implementation. It’s a process of iteration and refinement, and every hour spent here is another hidden cost as well as risking security if not done correctly.  

Scaling complexities  

Scaling open-source tools is where things often get complicated. Beyond everything already mentioned, your team will need to ensure the solution can handle growth. For many organizations, this means deploying on Kubernetes. But with Kubernetes comes steep learning curves and operational challenges. Making sure you always have a knowledgeable engineer available to handle unexpected issues and downtimes can become a challenge. Extended downtime due to this is a hidden cost since business users are impacted as they become reliant on your insights.    

Buying a managed solution  

A managed solution for Airflow and dbt can solve many of the problems that come with building your own solution from open-source tools such as: hassle-free maintenance, improved UI/UX experience, and integrated functionality. Let’s take a look at the pros.

Pros:  

• Faster Time to Value: With a managed solution, your team can get up and running quickly without spending weeks—or months—on setup and configuration.  

• Reduced Operational Overhead: Managed providers handle infrastructure, maintenance, and upgrades, freeing your team to focus on business objectives rather than operational minutiae.  

• Predictable Costs: Managed solutions typically come with transparent pricing models, which can make budgeting simpler compared to the variable costs of in-house built tooling.  

• Reliability: Your team is using version 1000+ of a managed solution vs the 1^st version of your self-managed solution. This provides reliability and peace of mind that edge cases have been handled, and security is under wraps.  

Cons:  

• Potentially Less Flexibility: This is the biggest con and reason why many teams choose to build. Managed solutions may not allow for the same level of customization as building in-house, which could limit certain niche use cases. Care must be taken to choose a provider that is built for enterprise level flexibility.

• Dependency on a Vendor: Relying on a vendor for your analytics stack introduces some level of risk, such as service disruptions or limited migration paths if you decide to switch providers. Some managed solution providers simply offer the service, but leave it up to your team to “make it work” and troubleshoot. A good provider will have a vested interest in your success, because they can’t afford for you to fail.  

Example:  

Using a solution like MWAA, teams can leverage managed Airflow eliminating the need for infrastructure worries however additional configuration and development will be needed for teams to leverage it with dbt and to troubleshoot infrastructure issues suck as containers running out of memory.

Datacoves for Airflow and dbt: The buy that feels like a build

For data teams, the allure of a custom-built solution often lies in its promise of complete control and customization. However, building this requires significant time, expertise, and ongoing maintenance. Datacoves bridges the gap between custom-built flexibility and the simplicity of managed services, offering the best of both worlds.

With Datacoves, teams can leverage managed Airflow and pre-configured dbt environments to eliminate the operational burden of infrastructure setup and maintenance. This allows data teams to focus on what truly matters—delivering insights and driving business decisions—without being bogged down by tool management.

Unlike other managed solutions for dbt or Airflow, which often compromise on flexibility for the sake of simplicity, Datacoves retains the adaptability that custom builds are known for. By combining this flexibility with the ease and efficiency of managed services, Datacoves empowers teams to accelerate their analytics workflows while ensuring scalability and control.

Datacoves doesn’t just run the open-source solutions, but through real-world implementations, the platform has been molded to handle enterprise complexity while simplifying project onboarding. With Datacoves, teams don’t have to compromize on features like Datacoves-Mesh (aka dbt-mesh), column level lineage, GenAI, Semantic Layer, etc. Best of all, the company’s goal is to make you successful and remove hosting complexity without introducing vendor lock-in. What Datacove does, you can do yourself if given enough time, experience, and money. Finally, for security concious organizations, Datacoves is the only solution on the market that can be deployed in your private cloud with white-glove enterprise support.

Datacoves isn’t just a platform—it’s a partnership designed to help your data team unlock their potential. With infrastructure taken care of, your team can focus on what they do best: generating actionable insights and maximizing your ROI.

Conclusion

The build vs. buy debate has long been a challenge for data teams, with building offering flexibility at the cost of complexity, and buying sacrificing flexibility for simplicity. As discussed earlier in the article, solutions like dbt and Airflow are powerful, but managing them in-house requires significant time, resources, and expertise. On the other hand, managed offerings like dbt Cloud and MWAA simplify operations but often limit customization and control.

Datacoves bridges this gap, providing a managed platform that delivers the flexibility and control of a custom build without the operational headaches. By eliminating the need to manage infrastructure, scaling, and security. Datacoves enables data teams to focus on what matters most: delivering actionable insights and driving business outcomes.

As highlighted in Fundamentals of Data Engineering, data teams should prioritize extracting value from data rather than managing the tools that support them. Datacoves embodies this principle, making the argument to build obsolete. Why spend weeks—or even months—building when you can have the customization and adaptability of a build with the ease of a buy? Datacoves is not just a solution; it’s a rethinking of how modern data teams operate, helping you achieve your goals faster, with fewer trade-offs.

Organizations often opt for open-source tools because "free" seems like an easy decision, especially compared to the higher price of managed versions of the same tooling. However, as with many things, there is no such thing as a free lunch. When choosing these open-source tools, it is easy to say that the Airflow and dbt pricing is $0 dollars meaning a cost-saving choice, but hidden expenses that are hard to ignore will quickly be revealed.

dbt Core and Apache Airflow are a natural pair in modern data analytics. dbt Core simplifies SQL-based data transformations, empowering data teams to create and maintain clean, well-documented, structured pipelines. Apache Airflow takes care of orchestrating these workflows, automating the movement and processing of data through the data engineering life cycle. Together, they can drive a powerful analytics stack that’s flexible and scalable—when used correctly. But this flexibility often comes at a price.  

In this article, we’ll examine the build vs. buy dilemma, highlighting the flexibility and true costs of open-source tools like dbt Core and Apache Airflow. We’ll also compare them to managed solutions such as dbt Cloud pricing and Datacoves pricing, providing the insights you need to evaluate the trade-offs and choose the best option for your organization.  

dbt and Airflow pricing

Open-source dbt pricing

The open-source tool dbt is free to download and use. However, the actual cost emerges when considering the technical resources required for effective implementation and management. Tasks such as setting up infrastructure, ensuring scalability, and maintaining the tool demand skilled engineers.

Assuming a team of 2–4 engineers is responsible for these tasks, with annual salaries ranging from $120,000 to $160,000 (approximately $10,000 to $13,000 per month), even dedicating 25–50% of their time to managing dbt Core results in a monthly cost of $5,000 to $26,000. As your use of dbt scales, you may need to hire a dedicated team to manage the open-source solution full-time, leading to costs equating to 100% of their combined salaries.

So we can begin to see the true open source dbt pricing, especially at scale. In addition to engineering labor are other costs such as time, and effort required to maintain and scale the platform. More on that later.

dbt Cloud Pricing

Just on engineering pricing alone, we begin to see the comparison between the open-source and managed solutions. dbt Labs offers a hosted solution, dbt Cloud, with added features and tiered pricing options.

• Developer Plan: Best for individual users, this free tier includes a single developer seat, up to 3,000 models built per month, and support for one dbt project.

• Team Plan: Aimed at small to mid-sized teams, this plan supports up to 8 developer seats, one dbt project, and 15,000 models built per month. Priced at $100/user/month, additional model builds cost $0.01 per model.

• Enterprise Plan: (This is the plan that most medium and large organizations will need) Designed for larger organizations, this tier supports unlimited users and projects. The MSRP jumps to a whopping $4,800 per seat and a fixed number of model runs after which the $0.01 per model price also applies.

Opting for a managed solution will allow your organization to cut engineering costs down or allow your engineers to focus on other projects. However, while dbt Cloud reduced the infrastructure burden a bit, it only focuses on the T of ELT. Meaning, you still need engineers to manage the other pieces of the stack which can result in a disconnected data pipeline.

Open-source Airflow

It is worth noting that some companies decide to use dbt cloud for the scheduler feature which can quickly become limiting as workflows become more complex. The next step is always a full fledged orchestrator such as Airflow.

Just like dbt Core, Apache Airflow is also free to use, but the true cost comes from deploying and maintaining it securely and at scale, which requires significant expertise, particularly in areas like Kubernetes, dependency management, and high-availability configurations.

Assuming 2–4 engineers with annual salaries between $130,000 and $170,000 (around $11,000 to $14,000 per month) dedicate 25–50% of their time to Airflow, the monthly cost ranges from $5,500 to $28,000. The pattern we saw with dbt Core rings true here as well. As your workflows grow, hiring a dedicated team to manage Airflow becomes necessary, leading to costs equating to 100% of their salaries.

Managed Airflow from AWS, MWAA

For teams looking to sidestep the complexities of managing Airflow in-house, managed solutions provide an appealing alternative:

• AWS Managed Workflows for Apache Airflow (MWAA): A managed Airflow service from Amazon, MWAA simplifies deployment and scaling but has variable pricing based on environment size and execution time, which can make costs unpredictable.

• Other Providers: Options like Astronomer and Google Cloud Composer offer similar managed Airflow solutions, each with unique features, performance considerations, and pricing structures.

A managed Airflow solution typically costs between $5,000 and $15,000 per year, depending on workload, resource requirements, and the number of Airflow instances. By choosing a managed solution, organizations can see cost savings in the infrastructure maintenance, overall maintenance stress and more.

The hidden costs of open-source tools

Setting up and managing infrastructure for Airflow and dbt Core isn’t as straightforward—or as “free”—as it might seem. The day-to-day work from managing Python virtual environments, keeping dependencies in check, and tackling scaling challenges require ongoing expertise and attention. In addition to salaries and benefits, what starts as an open-source experiment can quickly morph into a significant operational overhead full of hidden costs. Let’s dive into how by looking at time and expertise, security and compliance, and scaling complexities which, if not considered, can lead to possible side effects such as extended downtime, security issues and more.

Time and expertise

The time it takes to configure, customize, and maintain a complex open-source solution is often underestimated. It’s not until your team is deep in the weeds—resolving issues, figuring out integrations, and troubleshooting configurations—that the actual costs start to surface. With each passing day your ROI is threatened. You want to start gathering insights from your data as soon as possible. Datacoves helped Johnson and Johnson set up their data stack in weeks

And then there’s the learning curve. Not all engineers on your team will be senior, and turnover is inevitable. New hires will need time to get up to speed before they can contribute effectively. This is the human side of technology: while the tools themselves might move fast, people don’t. That ramp-up period, filled with training and trial-and-error, represents yet another hidden cost.

Security and compliance

Security and compliance add another layer of complexity. With open-source tools, your team is responsible for implementing best practices—like securely managing sensitive credentials with a solution like AWS Secrets Manager. Unlike managed solutions, these features don’t come prepackaged and need to be built integrated with the system.

Compliance is no different. Ensuring your solution meets enterprise governance requirements takes time, research, and careful implementation. It’s a process of iteration and refinement, and every hour spent here is another hidden cost as well as risking security if not done correctly.

Scaling complexities

Scaling open-source tools is where things often get complicated. Beyond everything already mentioned, your team will need to ensure the solution can handle growth. For many organizations, this means deploying on Kubernetes. But with Kubernetes comes steep learning curves and operational challenges. Making sure you always have a knowledgeable engineer available to handle unexpected issues and downtimes can become a challenge. Extended downtime due to this is a hidden cost since business user are impacted as they become reliant on your insights.  

Comparing build vs. buy: Key tradeoffs

Throughout this article, we have uncovered the true costs of open-source tools, bringing us to the critical decision between building in-house or buying a managed solution. Even after we have uncovered the actual cost of open-source, the decision isn’t just about price—it’s also about flexibility a custom build offers.

Managed solutions often adopt a one-size-fits-all approach designed to attract the widest range of customers. While this can simplify implementation for many organizations, it may not always meet the specific needs of your team. To make an informed decision, let’s examine the key advantages and challenges of each approach.

Building In-House

Pros:

• Customization: The biggest advantage of building in-house is the flexibility to customize the tool to fit your exact use case. You maintain full control, allowing you to align configurations with your organization’s unique needs. However, with great power comes great responsibility—your team must have a deep understanding of the tools, their options, and best practices.

• Control: Owning the entire stack gives your team the ability to integrate deeply with existing systems and workflows, ensuring seamless operation within your ecosystem.

• Cost Perception: Without licensing fees, building in-house may initially appear more cost-effective, particularly for smaller-scale deployments.

Cons:

• High Upfront Investment: Setting up infrastructure requires a significant time commitment from developers. Tasks like configuring environments, integrating tools like Git or S3 for Airflow DAG syncing, and debugging can consume weeks of developer hours.

• Operational Complexity: Ongoing maintenance—such as managing dependencies, handling upgrades, and ensuring reliability—can be overwhelming, especially as the system grows in complexity.

• Skill Gaps: Many teams underestimate the level of expertise needed to manage Kubernetes clusters, Python virtual environments, and secure credential storage systems like AWS Secrets Manager.

Example:
A team building Airflow in-house may spend weeks configuring a Kubernetes-backed deployment, managing Python dependencies, and setting up DAG synchronizing files via S3 or Git. While the outcome can be tailored to their needs, the time and expertise required represent a significant investment.

Buying a managed solution

Pros:

• Faster Time to Value: With a managed solution, your team can get up and running quickly without spending weeks—or months—on setup and configuration.

• Reduced Operational Overhead: Managed providers handle infrastructure, maintenance, and upgrades, freeing your team to focus on business objectives rather than operational minutiae.

• Predictable Costs: Managed solutions typically come with transparent pricing models, which can make budgeting simpler compared to the variable costs of in-house built tooling.

Cons:

• Potentially Less Flexibility: Managed solutions may not allow for the same level of customization as building in-house, which could limit certain niche use cases.

• Dependency on a Vendor: Relying on a vendor for your analytics stack introduces some level of risk, such as service disruptions or limited migration paths if you decide to switch providers.

Example:

Using a solution like MWAA, teams can leverage managed Airflow eliminating the need for infrastructure worries however it may not have the flexibility or interoperability with other aspects of their stack

Whereas using a solution like Datacoves, teams can leverage managed Airflow and pre-configured environments for dbt Core. This eliminates the need for infrastructure setup, simplifies day-to-day operations, and allows teams to focus on deriving value from their analytics, not maintaining the tools that support them.  

Verdict on build vs buy

There is no universal right answer to the build vs. buy dilemma—every use case is unique. However, it’s important to recognize that many problems have already been solved. Unless there is a compelling reason to reinvent the wheel, leveraging existing solutions can save time, money, and effort.

In Fundamentals of Data Engineering, Joe Reis and Matt Housley emphasize the importance of focusing on delivering insights rather than getting entangled in the complexities of building and maintaining data infrastructure. They advocate for using existing solutions wherever possible to streamline processes and allow teams to concentrate on extracting value from data. The key question to ask is: Will building this solution provide your organization with a competitive edge? If the answer is no, it’s worth seeking out an existing solution that fits your needs. Managed platforms can reduce the need for dedicated personnel as we saw above and provide predictable costs, making them an attractive option for many teams.

This philosophy underpins why we built Datacoves. We believe data teams shouldn’t be bogged down by the operational complexities of tools like dbt and Airflow. And we also believe that Data teams should have access to the flexibility a custom-built solution has to offer. Datacoves offers the flexibility these tools are known for while removing the infrastructure burden, enabling your team to focus on what really matters: generating actionable insights that drive your organization forward.  

Why teams choose Datacoves for dbt and Airflow  

Simplifying complex analytics stacks

Datacoves delivers the best of both worlds: the flexibility of a custom-built open-source solution combined with the rich features and zero-infrastructure maintenance of a managed platform—all with minimal vendor lock-in. How does Datacoves achieve this? By focusing on open-source tools and eliminating the burden of maintenance. Datacoves has already done the challenging work of identifying the best tools for the job, configuring them to work seamlessly together, and optimizing performance.

With Datacoves, your team can stop worrying about infrastructure and focus entirely on generating insights. The platform includes bundled in-browser VS Code, dbt Core, and Python extensions, alongside ready-to-use virtual environments tailored to analytics needs. Add to this a fully managed Airflow experience, and you have a solution where the code remains yours, but the operational headaches are gone.

Managed Airflow with unique features

Datacoves has enhanced Airflow with features designed to make DAG development more intuitive and enjoyable:

• Flexible Airflow: Datacoves can be used with or without dbt, making it an ideal choice for teams seeking a robust, scalable Airflow solution without the hassle of managing it themselves. Anything you can do with open-source Airflow, you can do with Datacoves—without requiring a dedicated team.

• Shared “Team Airflow” for Collaboration: This shared space is designed for developing and testing DAGs in a production-like environment, ensuring that buggy DAGs don’t disrupt your production workflows. Team members can collaborate in an isolated development environment, enabling faster iteration and testing.

• Developer-Specific “My Airflow” for Isolated Workflows: Recognizing the limitations of a shared environment, Datacoves created “My Airflow,” a standalone instance for individual developers. Changes made to DAGs immediately reflect in this instance, allowing for rapid iteration and testing before moving to “Team Airflow” for more robust validation.

• Streamlined DAG Deployment (S3/Git Sync): Whether syncing DAGs via S3 or Git, Datacoves simplifies the process, eliminating common deployment pain points associated with Airflow orchestration.

Enterprise-grade infrastructure

• Kubernetes-Backed Scalability: Building a scalable platform often requires Kubernetes expertise, which can be costly and time-intensive. Datacoves eliminates this need with a skilled team that manages Kubernetes deployments, handling upgrades, maintenance, and downtime risks for you.

• SaaS and Private Cloud Options: Datacoves offers deployment flexibility, accommodating enterprises with strict security requirements. Choose between a SaaS offering or a private deployment within your organization’s VPC, depending on your needs.

Cost predictability

One of the key benefits of Datacoves is the elimination of hidden costs through its all-in-one platform approach. Teams often realize too late that piecing together the modern data stack—combining open-source tools, hosting solutions, and server infrastructure—results in unpredictable costs. A single misstep in configuration can lead to high cloud bills.

Datacoves removes the guesswork. Its optimized infrastructure provides predictable billing for non-variable services, along with clear guidelines for variable costs. By implementing best practices and optimizations, Datacoves ensures that your costs remain as low as possible without sacrificing performance.

Datacoves makes it easier for teams to harness the power of open-source tools like dbt and Airflow, without the operational burden. From simplifying complex workflows to delivering enterprise-grade infrastructure and predictable costs, Datacoves empowers teams to focus on what matters most: driving insights and business value.

Conclusion

Open-source tools like Airflow are incredibly powerful, offering flexibility and extensibility that modern analytics teams need. However, as we have seen, the initial appeal of "free" tools is not true. Actual costs exist in the form of salaries and benefits and hidden costs like costs of implementation, scaling, and long-term maintenance are very real and expensive. Paid solutions are around for a reason and finding the best one that suits your needs is essential.  

The most flexible managed data platform on the market

If your team is looking to scale its analytics stack without the operational burden of managing open-source tools, Datacoves offers the perfect balance of flexibility, simplicity, and cost-efficiency.  Explore Datacoves to learn more about our all-in-one platform for dbt Core and Airflow or check out our case studies and testimonials to see how other teams have accelerated their analytics engineering journey with Datacoves.

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SQL databases are great for organizing, storing, and retrieving structured data essential to modern business operations. These databases use Structured Query Language (SQL), a gold standard tool for managing and manipulating data, which is universally recognized for its reliability and robustness in handling complex queries and vast datasets.

SQL is so instrumental to database management that databases are often categorized based on their use of SQL. This has led to the distinction between SQL databases, which use Structured Query Language for managing data, and NoSQL databases, which do not rely on SQL and are designed for handling unstructured data and different data storage models. If you are looking to compare SQL databases or just want to deepen your understanding of these essential tools, this article is just for you.

What is an Open Source Database?  

Open source databases are software systems whose source code is publicly available for anyone to view, modify, and enhance. This article covers strictly open source SQL databases. Why? Because we believe that they bring additional advantages that are reshaping the data management space. Unlike proprietary databases that can be expensive and restrictive, open source databases are developed through collaboration and innovation at their core. This not only eliminates licensing fees but also creates a rich environment of community-driven enhancements. Contributors from around the globe work to refine and evolve these databases, ensuring they are equipped to meet the evolving demands of the data landscape.  

Why Use Open Source Databases?

Cost-effectiveness: Most open source databases are free to use, which can significantly reduce the total cost of ownership.

Flexibility and Customization: Users can modify the database software to meet their specific needs, a benefit not always available with proprietary software.

Community Support: Robust communities contribute to the development and security of these databases, often releasing updates and security patches faster than traditional software vendors.

OLTP vs OLAP

When selecting a database, it is important to determine your primary use case. Are you frequently creating, updating, or deleting data? Or do you need to analyze large volumes of archived data that doesn't change often? The answer should guide the type of database system you choose to implement.  

In this article we will be touching on OLTP and OLAP open source SQL databases. These databases are structured in different ways depending on the action they wish to prioritize analytics, transactions, or a hybrid of the two.  

What is OLTP?

OLTP or Online Transaction Processing databases are designed to manage and handle high volumes of small transactions such as inserting, updating, and/or deleting small amounts of data in a database. OLTP databases can handle real-time transactional tasks due to their emphasis on speed and reliability. The design of OLTP databases is highly normalized to reduce redundancy and optimizes update/insert/delete performance. OLTP databases can be used for analytics but this is not recommended since better databases suited for analytics exist.

Characteristics of OLTP:

• Handles large numbers of transactions by many users.
• Operations are typically simple (e.g., updating a record or retrieving specific record details).
• Focus on quick query processing and maintaining data integrity in multi-access environments.
• Data is highly normalized.

When to use OLTP?

Use OLTP if you are developing applications that require fast, reliable, and secure transaction processing. Common use cases include but are not limited to:  

E-commerce: Order placement, payment processing, customer profile management, and shopping cart updates.

Banking: Account transactions, loan processing, ATM operations, and fraud detection.

Customer Relationship Management (CRM): Tracking customer interactions, updating sales pipelines, managing customer support tickets, and monitoring marketing campaigns.

What is OLAP?

OLAP or Online Analytical Processing databases are designed to perform complex analyses and queries on large volumes of data. They are optimized for read-heavy scenarios where queries are often complicated and involve aggregations such as sums and averages across many datasets. OLAP databases are typically denormalized, which improves query performance but come with the added expense of storage space and slower update speeds.

Characteristics of OLAP:

• Designed for analysis and reporting functions.
• Queries are complex and involve large volumes of data.
• Focus on maximizing query speed across large datasets.
• Data may be denormalized to expedite query processing.

When to use OLAP?

Use OLAP if you need to perform complex analysis on large datasets to gather insights and support decision making. Common use cases include but are not limited to:

Retail Sales Data Analysis: A retail chain consolidates nationwide sales data to analyze trends, product performance, and customer preferences.

Corporate Performance Monitoring: A multinational uses dashboards to track financial, human resources, and operational metrics for strategic decision-making.

Financial Analysis and Risk Management: A bank leverages an OLAP system for financial forecasting and risk analysis using complex data-driven calculations.

In practice, many businesses will use both types of systems: OLTP systems to handle day-to-day transactions and OLAP systems to analyze data accumulated from these transactions for business intelligence and reporting purposes.

Now that we are well versed in OLTP vs OLAP, let's dive into our open source databases!  

Open Source OLTP Databases

PostgreSQL

A row-oriented database, often considered the world’s most advanced open source database. PostgreSQL offers extensive features designed to handle a range of workloads from single machines to data warehouses or web services with many concurrent users.

Best Uses: Enterprise applications, complex queries, handling large volumes of data.

SQLite

SQLite is a popular choice for embedded database applications, being a self-contained, high-reliability, and full-featured SQL database engine. This database is a File-based database which means that they store data in a file (or set of files) on disk, rather than requiring a server-based backend. This approach has several key characteristics and advantages such as being lightweight, portable, easy to use, and self-contained.

Best Uses: Mobile applications, small to medium-sized websites, and desktop applications.

MariaDB

A columnar database and offshoot of MySQL. MariaDB was created by the original developers of MySQL after concerns over its acquisition by Oracle. It is widely respected for its performance and robustness.

Best Uses: Web-based applications, cloud environments, or as a replacement for MySQL.

Firebird

Firebird is a flexible relational database offering many ANSI SQL standard features that run on Linux, Windows, and a variety of Unix platforms. This database can handle a hybrid approach of OLTP and OLAP due to its multi-generational architecture and because readers do not block writers when accessing the same data.

Best Uses: Small to medium enterprise applications, particularly where complex, customizable database systems are required.

Open Source OLAP Databases

ClickHouse

Known for its speed, ClickHouse is an open-source column-oriented, File-based database management system that is great at real-time query processing over large datasets.  As mentioned earlier in the article, File-based databases bring many benefits They make use of data compression, disk storage of data, parallel processing on multiple cores, distributed processing on multiple servers and more.

Best Uses: Real-time analytics and managing large volumes of data.

DuckDB

Similar to SQLite, DuckDB is an embedded file-based database, however, DuckDB is a column-oriented database that is designed to execute analytical SQL queries fast and efficiently. This database has no dependencies making it a simple, efficient, and portable database. Since it is file-based this means DuckDB runs embedded within the host process, which allows for high-speed data transfer during analytics.

Best Uses: Analytical applications that require fast, in-process SQL querying capabilities.

StarRocks

StarRocks is a performance-oriented, columnar distributed data warehouse designed to handle real-time analytics. StarRocks also supports hybrid row-column storage. It is known for its blazing-fast massively parallel processing (MPP) abilities. Data can be ingested at a high speed and updated and deleted in real time making it perfect for real-time analytics on fresh data.

Best Uses: Real-time analytical processing on large-scale datasets.

Doris

Doris is an MPP-based, column-oriented data warehouse, aimed at providing high performance and real-time analytical processing. Doris can support highly concurrent point query scenarios and high-throughput complex analytic scenarios. Its high speed and ease of use despite working with large amounts of data make it a great option.

Best Uses: Real-time OLAP applications and scenarios demanding fast data processing and complex aggregation.

Trino

Even though Trino is not a database, but rather a query engine that allows you to query your databases, we felt it is a powerful addition to this open source list. Originally developed by Facebook and known as PrestoSQL, Trino is designed to query large data warehouses and big data systems rapidly. Since it is great for working with terabytes or petabytes of data it is an alternative to tools such as Hive or Pig. However, it can also operate on traditional relational databases and other data sources such as Cassandra. One major benefit is that Trino allows you to perform queries across different databases and data sources. This is known as query federation.  

Best Uses: Distributed SQL querying for big data solutions.

Citus

While this is not a separate open source database, we felt it was a good addition to the list because Citus is an extension to PostgreSQL that that transforms your Postgres database into a distributed database. This enables it to scale horizontally.

Best Uses: Scalable PostgreSQL applications, especially those needing to handle multi-tenant applications and real-time analytics over large datasets.

Conclusion  

Open source SQL databases provide a variety of options for organizations and developers seeking flexible, cost-effective solutions for data management. Whether your needs are for handling large data sets, real-time analytics, or robust enterprise applications, there is likely an open source database out there for you.

Data is in the spotlight as companies everywhere realize data's true potential. With big initiatives like GenAI and sophisticated data ecosystems, ensuring data quality is not just a necessity but a mandatory investment for businesses and analysts worldwide. Some people are learning the hard way that you need stable data foundations to get the results these initiatives promise.

While there are many great tools out there, the spotlight on open source tools has never been brighter. Open source software offers transparency, adaptability, and community-driven enhancements that are crucial in the rapidly evolving data landscape. This article covers 5 open source data quality tools and is current as of April 2024, so if that is something that interests you, stick around.

What is data quality?

First things first, what is data quality?  There are many definitions of data quality, but data is considered high quality if it is fit for its intended uses in operations, decision-making, and planning. In other words, data quality refers to the data's accuracy, completeness, reliability, relevance, and how up-to-date it is. In the context of data-driven decision-making, high-quality data is crucial as it directly impacts the accuracy of insights and the effectiveness of decisions. Our data foundation.

High-quality data should be:

Accurate: Data that is free from errors and discrepancies.

Complete: Data that covers the necessary breadth and depth needed by the business.

Reliable: Data that has no missing elements and is consistently represented and sourced.

Relevant: Data that is applicable to the context and purposes for the business.

Current: Data that is up-to-date and timely for its purpose.

Understanding what is needed for data quality is the first step toward recognizing the importance of these tools and practices that maintain or enhance this quality.

What should you track for good data quality?

Now we know what constitutes high quality data but what do we need to monitor to ensure that our data is high quality? The good news is these metrics tend to be universal. For maintaining high data quality, several metrics and elements should be monitored regularly:

Accuracy: Ensure that your data correctly represents reality or the source from which it came.

Completeness: Check for missing values or data segments that could lead to incorrect analysis or conclusions.

Consistency: Data across different systems or platforms should match and be consistent.

Timeliness: Data should be updated and available in a timeframe that aligns with its intended use.

Validity: Data should adhere to the relevant rules, such as data formats and value ranges.

Uniqueness: No duplicates should be present unless necessary, ensuring each entry is unique.

Integrity: There should be a relationship between datasets and records that maintains data accuracy and consistency.

By tracking these metrics, organizations can set up the essential data foundation and significantly improve the trustworthiness and utility of their data. This will lead to better outcomes and insights that can support great data initiatives of the future.  

Since it is essential to track these metrics, companies are on the search for the best tool to help them improve their data quality. Here is a list of open source tools that can be leveraged to improve data quality.

Why open source?

Before we jump into the tool list you may have noticed that a quick google search for this topic will give me many different lists. How is our list different? Well, we are focusing on open source tools. There are many great tools out there both paid and “free” and we put quotes around free because there is no such thing as free; there are always hidden costs (hours worked) for setup and maintenance. However, we wanted to make this open source tool list because regardless of the hidden costs we believe in the following benefits of open source tools:

Transparency: Open source tools offer complete transparency in their operations and algorithms. Users can inspect, modify, and improve the code, which enhances trust and reliability.

Community: Open source projects benefit from the collective intelligence of a global community. This not only accelerates innovation and bug resolution but also provides a large pool of knowledge and support.

Flexibility: With open source, organizations are not locked into proprietary systems, allowing them to tailor tools to their specific needs and integrate them seamlessly into their existing environments.

Cost-effectiveness: While open source doesn't always mean free, it significantly reduces costs associated with licensing fees and vendor lock-in, making cutting-edge tools accessible to everyone.

Quality and Security: Continuous contributions and scrutiny by the community mean that open source tools often meet high standards of quality and security, with issues being identified and addressed rapidly.

Our selection of open source data quality tools is grounded in rigorous open source criteria. We believe that the strength of an open source project lies not just in its ability to solve complex problems but also in its community, transparency, and commitment to ongoing improvement. When compiling this list, we considered factors such as active community engagement, frequency of updates, the quality of the documentation, and ease of contribution. This ensures that the tools recommended not only meet high standards of performance and reliability but also embody the principles that make open source software a valuable asset to the data quality landscape. So without further ado, let's jump into our list.

Open source data quality tool list

dbt Core

Self managed or in Datacoves

Primary Language: SQL / YAML

Purpose & Features: dbt core is an open source tool that allows data analysts and engineers to transform data in their data warehouses by writing dynamic SQL queries, which dbt then converts into tables and views. It also supports version control, testing, and documentation, which helps maintain data integrity and reliability.  

For data quality, dbt Core has some out of the box data tests which can be extended through custom made test, or by using libraries such as dbt-expectations and elementary. Testing is easily done by configuring macros in YAML files or by writing custom SQL tests. However, integrating dbt Core into your data stack can be a big task especially when it comes to scheduling. A managed dbt Core platform such as Datacoves could be a great option for saving time and money. While dbt handles only the 'T' in ELT, Datacoves’ managed dbt Core Platform ensures that the entire ELT process is smooth and interconnected, allowing your team to concentrate on deriving insights from the data. There are other dbt alternatives on the market that can also be explored which handle the "T" in the ELT process.

Who it is for: Best for teams using SQL who want to transform data directly in the warehouse and who want to follow software development best practices including unit testing in their data pipelines.

Soda Core

Primary Language: YAML

Purpose & Features: Soda Core is the open source component that allows users to define data quality checks in code and integrate them into workflows.  

Who it's for: Teams that need data quality checks integrated into their existing Python workflows or data pipelines.

Great Expectations

Primary Language: Python  

Purpose & Features: This tool is a data quality platform that allows you to create data tests, documentation, and profiles automatically. It easily integrates into existing data processing pipelines to ensure data validation against expectations (unit tests). You can collaborate with nontechnical stakeholders by sharing the Data Docs. Data docs are Expectations, Validation Results, and other metadata translated into a human readable format as seen in the image below.  

Who it's for: Data teams looking for a Pythonic way to enforce data quality rules and create automated data documentation.

Deequ

Primary Language: Scala (for Apache Spark)

Purpose & Features: Deequ is an open source tool by Amazon with which you can define "unit tests" (columnar or row level) for large-scale data within the Spark ecosystem. It allows for automated checks of data quality metrics such as completeness, uniqueness, and conformity. This enables data teams to find errors early before they are consumed downstream. You can use Deequ to define your assumptions about the data in unit tests to catch any data that does not meet your assumptions. This tool works on tabular data such as CSV files, databases tables, logs and flattened JSON files.  

Who it's for: Data engineers and scientists working with big data in Spark (billons of rows), particularly those focused on maintaining data quality at scale.

Data Diff

Primary Language: You don’t manually write data quality tests but as you make changes to your SQL data diff will work its magic.  

Purpose & Features: This tool is a little different from the rest because you're not exactly writing tests to catch data quality issues. Instead, this open source Python package by Datafold lets you do development testing by spotting the differences between tables whenever you tweak your code. It's a great way to compare what's happening in your production data against your development changes, helping you see directly how those code changes are playing out in the data.  

Who it's for: Data engineers and teams who need to ensure that changes in data processing and ETL logic do not negatively affect data quality.

Choose the right open source data quality tool for your use case

The concept of "the best" for data quality tools is inherently tied to specific use cases. What might be an ideal solution for one organization could be less effective for another, depending on the unique challenges and requirements each face.

Before you dive into a tool, it's crucial to understand your organization's specific data quality challenges. Are you dealing with high volumes of data, requiring scalability? Or are your main issues related to data consistency and accuracy in a smaller, more controlled dataset? Identifying your primary use case will help you navigate through our top 10 tools and select the one that best fits your situation.  

Here’s 6 tips to help you make an informed choice for your specific needs:

1. Assess Your Data Quality Needs:

Identify the primary issues you're facing with your data. Are you struggling with incomplete data, inconsistencies, outdated information, or data that's not in the right format? Understanding your main challenges will guide you toward a tool that specializes in addressing those specific problems. Once you understand your data quality challenges and objectives, match these with the strengths of the tools listed above

2. Consider Your Technical Environment:

Evaluate the technical stack you are currently using. Some data quality tools are better suited for certain environments or integrate more seamlessly with specific databases, data lakes, or processing frameworks. Choose a tool that aligns with your existing infrastructure to reduce integration headaches.

3. Evaluate Community and Support:

The strength of an open-source tool lies in its community. Look for a tool with an active community, which is evident through regular updates, vibrant forums, and extensive documentation. A strong community can provide invaluable support, from troubleshooting to best practices.

4. Check for Flexibility and Scalability:

Your data needs will evolve, so it’s important to choose a tool that is flexible and can scale with your business. Assess the tool’s ability to handle different data volumes, types, and sources. A good open-source tool should not only solve your current data quality issues but also adapt to future challenges.

5. Review Security and Compliance Features:

Data security and compliance are imperative. Be sure the tool complies with data protection regulations and offers security features to protect your data. This is especially important if you're dealing with sensitive or personal information.

6. Test Drive the Tool:

Finally, don’t hesitate to get your hands dirty. Most open-source tools are free to use, so take advantage of this by testing the tool with your data. This will give you a clear idea of the tool’s usability, effectiveness, and fit with your use case. Be sure to go into this with an open mind to get the most out of the tool.

Conclusion

In the era of generative AI and other lofty initiatives high-quality data is not just an option but a necessity, and embracing these open-source data quality tools can significantly enhance the reliability and accuracy of your data. Remember, the "best" tool is one that aligns closely with your specific use case offering the features and flexibility your team needs to effectively tackle your data quality challenges; it very well could be a combination of these tools. Whether you are in the world of SQL, Python, or any other programming language, there is a tool tailored to your needs. Consider factors such as ease of integration into your current data ecosystem, the learning curve for your team, and the level of community support available.

Data teams deciding on data transformation tools need to consider various aspects before deciding on how they will develop and orchestrate data pipelines. They also need to accelerate infrastructure deployment to deliver at the pace the business requires. 

The hurdle to overcome is that doing this well requires a lot of rethinking of legacy processes and technology. 

Implementing DataOps, CI/CD, and setting up an ETL or ELT isn’t a straightforward process, which is why teams often go with an incremental approach or set up the basics and end up with technical debt that accumulates substantially over time. 

In this article, we’ll go through a list of 10 data transformation tools that will help you get the job done. If you are in the process of evaluating your next ETL/ELT platforms, this article for you.

Side Note: As data professionals, we’ve been around since the early days of data transformation and noticed many flaws within the entire process. There’s a steep learning curve: adding a single tool to the workflow can quickly multiply into a tech stack with multiple SaaS platforms. That’s why we built Datacoves to help you bring everything together to accelerate time to value. If you’d like to learn more about how Datacoves helps you develop and orchestrate data pipelines, you can schedule a free demo here. 

Data transformation in the End-to End process

Data transformation is the process of converting data from one format or structure to another. It improves the performance of data processing systems and compliance with data governance regulations.  

Data transformation is just one of the steps on the road to deriving value from data.

The end-to-end process includes the following steps:

• Data Extraction: To extract data from sources such as databases and APIs 
• Data Loading:  To load data into a desired destination such as a Data Warehouse.
• Data Transformation: To cleanse and transform data into usable insights based on business needs. 
• Data Orchestration: To schedule and automate the end-to-end process
• Data Delivery: To visualize and support decision-making 
• Data Observability: To view and get alerted when data issues occur

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It’s worth taking each of these steps into consideration when determining the best data transformation tool for your organization.

There is a common misconception that the tool alone will solve all the problems. 

However, using the right tools without addressing the underlying processes can lead to a data mess that can exacerbate the underlying issue, costing more time and money. This data mess could easily be avoided in the first place, not just by having the right tools but by also having the modern best practices in place.

What is the difference between ETL and ELT? 

Both help businesses extract, load, and transform data, but the sequence of events is different with their pros and cons.

• ETL Process: The traditional approach to data transformation. Data is Extracted and Transformed before it gets Loaded. 
• ELT Process: The modern approach to data transformation. Data is Extracted and Loaded before it gets Transformed. 

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ELT is generally more effective than ETL processes because it removes the uncertainty of not having the necessary data for future use cases and offers more flexibility in the long term. Since storage is typically affordable, it makes more sense to simplify the ingestion process.

10 best data transformation tools

Here’s a list of the top data transformation tools to manage the ETL process: 

• Datacoves
• dbt Cloud
• Apache Airflow
• SAS
• SQLMesh
• Informatica
• Talend
• Azure Data Factory
• Matillion
• Alteryx

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Each of these tools falls into one of two categories: code-based or visual/drag-and-drop interface. Both have their own set of pros and cons, which we’ll go through below. 

Code-based tools for data transformation 

Code-based tools allow you to transform data by using SQL or Python to explicitly define the transformation steps. Although it requires knowledge and experience, visual tools don’t negate the need to know SQL. This approach gives users a high degree of flexibility and control, and simplifies the maintainability and validation of work before releasing it to production. 

Moreover, it is simpler to trace each data transformation step without having a disconnected document explaining what the transformation “should” do.

1. Datacoves

After having multiple conversations with data teams at enterprise companies, the challenge of developing and orchestrating dbt pipelines is a topic that has come up on numerous occasions. 

There are a lot of tools to figure out when it comes to implementing the best practices for digital transformations and custom applications. It’s not uncommon for companies to end up with more than one SaaS platform and tool than they had initially planned. We built Datacoves to eliminate this need by providing the following: 

• Managed dbt Core
• Open-source technologies, meaning there is no vendor lock-in
• Managed SaaS or private cloud deployment

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Datacoves focuses on helping companies accelerate growth by providing a complete ELT solution, including orchestration and visualization. Therefore, the learning curve for data transformation is minimized because of our best-practice accelerators and the available tool integrations to form an end-to-end platform. 

Top features

• Managed dbt Core: Get full access to dbt through Datacoves, where we provide a structured process for developing dbt pipelines. Configure a dbt environment where you can write data transformations in modular code so it’s easier to test and maintain. 
• Hosted dbt Documentation: Simplified dbt docs deployment
• Managed Airflow: Orchestrate data using Airflow, which is an integration that’s available to give companies the flexibility they need for an end-to-end process from data load to activation.
• VS Code in Browser: No software installation is required, allowing you to write and edit code from anywhere as long as you have access to a web browser.
• Deployable in Private Cloud: Accelerate data transformation and minimize ownership costs while complying with corporate and governmental regulations. 
• Internal Tool Integrations: Integrate with internal tools like Active Directory, Bitbucket, Jenkins, GitLab, and more. 
• CI/CD: Deliver high-quality data to users faster with more reliability and efficiency. 

How data transformation works with Datacoves

Here is the extended version of the ELT process with Datacoves: 

• Extract data
• Load data
• Transform data
• Observe
• Orchestrate
• Analyze 

Develop modular code and track version changes that you and your team can view. You’re also able to validate the quality of data transformations with our built-in testing frameworks and generate documents to leave a record of how you’re transforming data.

You develop in a VS Code environment that can be configured with a vast array of VS Code extensions and Python libraries All the modern data tools you need are provided in a structured workspace: 

• GitHub workflows
• Automation scripts
• Loading
• Scheduling
• Data security
• Data transformation 

Is Datacoves right for you?

It’s suitable for medium and large companies that lack the expertise or don’t want to create and manage complex data processes and need the flexibility that complex enterprise processes require. 

Data teams can use all the components provided within the dbt ecosystem in a structured, methodical way with Datacoves. This means you’ll have a simplified dbt experience, yet you’ll still see the same results of dbt when used to its full potential. 

Smaller companies also gain competitive advantages with Datacoves because they’ll be able to implement DataOps, follow best practices, and get a fully managed VS Code environment accelerating time to value. 

If you would like to know more about how Datacoves can help, you can schedule a demo here. 

2. dbt Cloud

dbt Cloud allows businesses to build and maintain data pipelines. It’s a cloud-based platform with a web-based IDE that allows you to transform data within a cloud data warehouse. They can help you reduce the time spent setting up an end-to-end solution.

Notable features 

• Modular Code: Write data transformations in modular code. 
• Version Control: Monitor changes to your code and seamlessly collaborate with team members. 
• Data Testing: Built-in testing framework for validating the quality of data transformations. 
• Documentation: Generate documents for data transformations so you know how your data is being transformed. 
• CI/CD: Streamline the data transformation process. 

Is dbt Cloud right for you? 

dbt Cloud works well for organizations looking to reduce the time and effort required to transform data pipelines. 

Since dbt Cloud is a web-based IDE, it may feel limited for data teams that would rather use a VS Code environment. Moreover, dbt is not deployable in a company’s private cloud. It also typically requires other SaaS tools for complicated data pipelines, making it more difficult to manage unless you have the necessary integration experience with each of those SaaS tools. 

Most importantly, dbt Cloud is focused solely on the data transformation step of the ELT process. Hence, you are unable to load VS Code extensions nor additional Python libraries. An enterprise with any level of complexity will also need a full-featured orchestrator. 

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3. Apache Airflow

Apache Airflow is an open-source platform for workflow management. You can orchestrate and schedule data pipelines. It’s a scalable and flexible platform that’s based on Python. You can also define your own operators with Airflow. 

Notable features 

• ELT pipelines: Airflow is a tool for organizing full ELT pipelines. But, it still requires your expertise to build them using Python. 
• CI/CD tools: You can use CI/CD tools to assist with deployment. 
• Data Extraction and Load: With airflow, you can create Python scripts that extract and load information from other data sources. 
• Python Libraries: Airflow can be paired with Python libraries like Pandas to shape and aggregate data.
• Machine learning: Schedule the training of machine learning models. 

Is Apache Airflow right for you?

Apache Airflow works well for those needing a scalable data transformation tool with an open-source platform. It’s particularly a good choice for businesses mainly using Python to manage their data. 

However, Airflow is primarily an orchestrator. That means you may end up building complex code in your data pipelines. Therefore, developing and maintaining this complexity requires experience and technical expertise. Managing the infrastructure for Airflow is not trivial and also requires an understanding of tools like Docker and Kubernetes.

4. SAS

SAS is a solution that allows you to transform and prepare data for analysis. It offers a wide range of features for data transformation, including data cleaning, data integration, and data mining.

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Notable features

• Data Cleaning: Remove duplicate records, correct errors, complete missing values, and so forth. 
• Data Integration: Combine data from different departments or systems into a single dataset. 
• Data Mining: Identify patterns and trends in your data. 
• Data Visualization: Create charts and graphs to visualize data. 

Is SAS right for you?

SAS is ideal for companies with complex datasets, such as those in financial services, healthcare, and retail industries. Additionally, it’s ideal for professionals with advanced skills and knowledge in data transformation. 

With that in mind, there are better solutions than SAS for those less experienced in programming and data management, as SAS licensing can be quite expensive.  

5. SQLMesh

SQLMesh is a complete DataOps solution for data testing and transformation. Teams can use SQLMesh to collaborate on data pipelines when transforming data. 

Notable features

• Semantic Understanding: SQLMesh can understand the SQL written so you can write code efficiently and avoid errors.
• Simplified CI/CD: SQLMesh can identify the changes made to data pipelines and apply only the necessary updates to each environment.‍
• Column-level Lineage: Get a better understanding of the relationships between your data and the transformation process.‍
• Transpilation: Run your SQL on multiple engines so that it’s easier to migrate data into a new platform.

Is SQLMesh right for you?

SQLMesh is well-suited for businesses with SQL and Python expertise that need to collaborate on complex data transformations and pipelines. Although other open-source tools are available, teams can use SQLMesh to maintain data quality and perform unit testing of their transformations.

SQLMesh may not be ideal when you only need to perform simple data transformations. In this case, there are other more straightforward tools available. Moreover, SQLMesh may not be for you when your primary focus is on real-time data processing.

Visual ELT tools for data transformation

Visual tools make the ELT process more straightforward by removing the need to manually write code. It works by dragging and dropping pre-built components into a canvas. This makes them ideal for data teams who aren’t as experienced in programming. 

The biggest advantage of graphical tools for ETL is that people who are less comfortable with code can use them. Conversely, drag-and-drop tools typically don’t offer the same level of flexibility and control as code-based tools, which can complicate the process of debugging data pipelines and long-term maintenance.

6. Informatica

Informatica helps you turn your data into an asset. It’s a cloud-based or on-prem solution for data management with numerous data transformation libraries and APIs available. 

Notable features 

• PowerCenter: Enterprises can use this to manage large and complex data pipelines. 
• Cloud Data Integration: Cloud-based integrations that allow you to move data. 
• Data Engineering Integration: A solution designed to assist data engineers with code development, version control, and CI/CD.
• Data Engineering Streaming: Manage streaming data pipelines with data ingestion, processing, and visualization. 

Is Informatica right for you?

Informatica can be a good choice for large enterprises and data professionals looking to quickly transform large volumes of complex data using an on-premise solution. It can also be a good choice for companies that need to comply with industry-specific data standards. 

However, it may be too complicated to use for some organizations. Informatica requires a team of experienced data engineers with the necessary skills and experience. DataOps can also be a challenge. Since you’ll be dealing with multiple things simultaneously, it’s easy to get lost in the process when you don’t have the full technical expertise.

Moreover, it’s an expensive solution. There are other more affordable alternatives.

7. Talend

Talend is a cloud-native platform deployable on public cloud solutions such as AWS, Azure, and GCP. They also offer an on-prem solution and provide a variety of components and custom connectors for data transformation. 

Notable features 

• Talend Open Studio: An open-source data integration tool for smaller workloads. 
• Talend Data Fabric: Manage the data integration process. Maintain data quality and governance. 
• Cloud Data Integration: Cloud-based data integration service with a graphical user-friendly interface for creating and managing data transformation tasks. 
• Built-in Data Catalog: Discover new data assets across your organization. 

Is Talend right for you?

Talend works for most businesses and data professionals. It’s particularly well-suited for those who need to:

• Transform data from a variety of sources.
• Migrate data to a new system. 
• Build and maintain a data warehouse. 
• Check and resolve data quality issues 

Still, you may want to consider other options when prioritizing DataOps and performing highly specialized data transformations such as machine learning or NLP. Talend enterprise licenses may also be costly.

8. Azure Data Factory

Azure Data Factory helps you simplify the data transformation process at scale. You’re provided with a code-free and code-centric experience for orchestrating data transformation pipelines. 

Notable features 

• Built-in connectors: A variety of built-in connectors for popular data sources are available. 
• Data Orchestration: Schedule your data pipelines.
• Built-in Components: Use built-in components to reshape data.

Is Azure Data Factory right for you?

Azure Data Factory could be the right option for data professionals working within the Azure ecosystem. Azure may be worth considering when you’re looking into data warehousing using Azure Synapse and Azure DataOps and not just ELT. 

However, Azure Data Factory might not be the best option when you’re on a budget. As with any visual ELT tooling, DataOps and pipeline maintainability may be more complex leading to an increased total cost of ownership.

9. Matillion

Matillion is a cloud-based data transformation tool that provides you with on-premises databases, cloud applications, and SaaS platform integrations. 

Notable features

• Cloud-native architecture: Matillion runs in the cloud and allows you to push down transformation logic to leverage the scalability and performance of cloud data platforms such as Amazon Redshift and Snowflake. 
• Visual Interface: Create data pipelines using a graphical interface, reducing the need to write code. 
• Library of Connectors: Access a library of pre-built transformations and connectors across a range of data sources. 
• High-Code and No-Code: Supports both hide-code and no-code development, making it accessible for beginner and intermediate users. 
• dbt Component: With the dbt component, you can embed dbt within a Matillion pipeline.

Is Matillion right for you?

Matillion’s pre-built connectors and visual interface makes it an ideal solution for less experienced data professionals. The disadvantage is that it can be costly for businesses on a budget. Moreover, you must ensure that Matillion supports your specific requirements and how you intend to perform the data transformations. Care must be given to the long-term maintainability of pipelines that are both visual and code-based.

Getting started with Matillion is simple because they use a drag-and-drop interface for building data pipelines. But like with any other visual tool, there is still a learning curve and it’s typical to have a mix of code and visual components in a production data pipeline.

10. Alteryx

Alteryx simplifies the data transformation process. You can automate advanced analytics and prepare data through self-service. It’s an effective solution that makes it easier for teams to collaborate. Unlike the other visual tools above which are typically used by Data Engineers in IT, Alteryx is more widely adopted in less technical departments of an organization. It’s also typically paired with visualization tools like Tableau. 

Notable features 

• Drag-and-drop User Interface: The visual interface makes building and collaborating on data transformation workflows easier.
• Data Loading: Connectors to popular databases and services allow you to integrate different data sources 
• Machine Learning: Alteryx may also be used to create simple machine learning models in a visual way

Is Alteryx right for you?

Alteryx is a good option to help ensure teams are on the same page throughout the data workflow. Data transformation projects can be shared and feedback provided seamlessly, making collaboration easier. 

The downside is that Alteryx is costly compared to the other tools on this list. Moreover, there is still a bit of a learning curve, even if you’re experienced in data analytics. You should also check that Alteryx aligns with teams for effective collaboration.

How Datacoves can help you transform data

Data transformation is a process that’s prone to multiple errors along the way. While many tools listed can help you reduce friction, they must be carefully evaluated. With Datacoves, you’ll be able to implement best data practices and DataOps so that you have a smooth process with a minimized learning curve. 

If you’d like to learn more about how Datacoves helps you accelerate time to value, you can schedule a free demo here.